As an exemplary self-supervised approach for representation learning, time-series contrastive learning has exhibited remarkable advancements in contemporary research. While recent contrastive learning strategies have focused on how to construct appropriate positives and negatives, in this study, we conduct theoretical analysis and find they have overlooked the fundamental issues: false negatives and class imbalance inherent in the InfoNCE loss-based framework. Therefore, we introduce a straightforward modification grounded in the SimCLR framework, universally adaptable to models engaged in the instance discrimination task. By constructing instance graphs to facilitate interactive learning among instances, we emulate supervised contrastive learning via the multiple-instances discrimination task, mitigating the harmful impact of false negatives. Moreover, leveraging the graph structure and few-labeled data, we perform semi-supervised consistency classification and enhance the representative ability of minority classes. We compared our method with the most popular time-series contrastive learning methods on four real-world time-series datasets and demonstrated our significant advantages in overall performance.

翻译：作为表征学习中一种典型的自监督方法，时间序列对比学习在当代研究中展现出显著进展。尽管近期对比学习策略聚焦于如何构建合适的正负样本，本研究通过理论分析发现，这些方法忽视了基于InfoNCE损失框架中存在的根本性问题：假阴性与类别不平衡。因此，我们基于SimCLR框架提出一种简洁的改进方案，该方案可普遍适用于执行实例判别任务的模型。通过构建实例图以促进实例间的交互学习，我们借助多实例判别任务模拟监督式对比学习，从而缓解假阴性的有害影响。此外，利用图结构及少量标注数据，我们执行半监督一致性分类，并增强少数类别的表征能力。在四个真实世界时间序列数据集上，我们将所提方法与最流行的时间序列对比学习方法进行对比，验证了其在整体性能上的显著优势。